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lead-hand-skill

Research prospective customers from public sources, add company and contact details, score their fit, and merge duplicate records. Organize the findings into prioritized lead lists with source information, using CSV, JSON, or Markdown tables.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Expert knowledge for AI lead generation — web research, enrichment, scoring, deduplication, and report generation

연동 도구LinkedIn
설치 방법 보기

이럴 때 유용합니다

  • Find companies matching an ideal customer profile
  • Add funding and hiring details to lead records
  • Score prospects to set sales priorities
  • Merge duplicate leads and export a list

포함된 파일(2)

  • SKILL.md6.9 KB
  • HAND.toml9.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Lead Generation Expert Knowledge

Ideal Customer Profile (ICP) Construction

A good ICP answers these questions:

  1. Industry: What vertical does your ideal customer operate in?
  2. Company size: How many employees? What revenue range?
  3. Geography: Where are they located?
  4. Technology: What tech stack do they use?
  5. Budget signals: Are they funded? Growing? Hiring?
  6. Decision-maker: Who has buying authority? (title, seniority)
  7. Pain points: What problems does your product solve for them?

Company Size Categories

CategoryEmployeesTypical BudgetSales Cycle
Startup1-50$1K-$25K/yr1-4 weeks
SMB50-500$25K-$250K/yr1-3 months
Enterprise500+$250K+/yr3-12 months

Web Research Techniques for Lead Discovery

Search Query Patterns

# Find companies in a vertical
"[industry] companies" site:crunchbase.com
"top [industry] startups [year]"
"[industry] companies [city/region]"

# Find decision-makers
"[title]" "[company]" site:linkedin.com
"[company] team" OR "[company] about us" OR "[company] leadership"

# Growth signals (high-intent leads)
"[company] hiring [role]" — indicates budget and growth
"[company] series [A/B/C]" — recently funded
"[company] expansion" OR "[company] new office"
"[company] product launch [year]"

# Technology signals
"[company] uses [technology]" OR "[company] built with [technology]"
site:stackshare.io "[company]"
site:builtwith.com "[company]"

Source Quality Ranking

  1. Company website (About/Team pages) — most reliable for personnel
  2. Crunchbase — funding, company details, leadership
  3. LinkedIn (public profiles) — titles, tenure, connections
  4. Press releases — announcements, partnerships, funding
  5. Job boards — hiring signals, tech stack requirements
  6. Industry directories — comprehensive company lists
  7. News articles — recent activity, reputation
  8. Social media — engagement, company culture

Lead Enrichment Patterns

Basic Enrichment (always available)

  • Full name (first + last)
  • Job title
  • Company name
  • Company website URL

Standard Enrichment

  • Company employee count (from About page, Crunchbase, or LinkedIn)
  • Company industry classification
  • Company founding year
  • Technology stack (from job postings, StackShare, BuiltWith)
  • Social profiles (LinkedIn URL, Twitter handle)
  • Company description (from meta tags or About page)

Deep Enrichment

  • Recent funding rounds (amount, investors, date)
  • Recent news mentions (last 90 days)
  • Key competitors
  • Estimated revenue range
  • Recent job postings (growth signals)
  • Company blog/content activity (engagement level)
  • Executive team changes

Email Pattern Discovery

Common corporate email formats (try in order):

  1. firstname@company.com (most common for small companies)
  2. firstname.lastname@company.com (most common for larger companies)
  3. first_initial+lastname@company.com (e.g., jsmith@)
  4. firstname+last_initial@company.com (e.g., johns@)

Note: NEVER send unsolicited emails. Email patterns are for reference only.


Lead Scoring Framework

Scoring Rubric (0-100)

ICP Match (30 points max):
  Industry match:     +10
  Company size match: +5
  Geography match:    +5
  Role/title match:   +10

Growth Signals (20 points max):
  Recent funding:     +8
  Actively hiring:    +6
  Product launch:     +3
  Press coverage:     +3

Enrichment Quality (20 points max):
  Email found:        +5
  LinkedIn found:     +5
  Full company data:  +5
  Tech stack known:   +5

Recency (15 points max):
  Active this month:  +15
  Active this quarter:+10
  Active this year:   +5
  No recent activity: +0

Accessibility (15 points max):
  Direct contact:     +15
  Company contact:    +10
  Social only:        +5
  No contact info:    +0

Score Interpretation

ScoreGradeAction
80-100AHot lead — prioritize outreach
60-79BWarm lead — nurture
40-59CCool lead — enrich further
0-39DCold lead — deprioritize

Deduplication Strategies

Matching Algorithm

  1. Exact match: Normalize company name (lowercase, strip Inc/LLC/Ltd) + person name
  2. Fuzzy match: Levenshtein distance < 2 on company name + same person
  3. Domain match: Same company website domain = same company
  4. Cross-source merge: Same person at same company from different sources → merge enrichment data

Normalization Rules

Company name:
  - Strip legal suffixes: Inc, LLC, Ltd, Corp, Co, GmbH, AG, SA
  - Lowercase
  - Remove "The" prefix
  - Collapse whitespace

Person name:
  - Lowercase
  - Remove middle names/initials
  - Handle "Bob" = "Robert", "Mike" = "Michael" (common nicknames)

Output Format Templates

CSV Format

Name,Title,Company,Company URL,LinkedIn,Industry,Size,Score,Discovered,Notes
"Jane Smith","VP Engineering","Acme Corp","https://acme.com","https://linkedin.com/in/janesmith","SaaS","SMB (120 employees)",85,"2025-01-15","Series B funded, hiring 5 engineers"

JSON Format

[
  {
    "name": "Jane Smith",
    "title": "VP Engineering",
    "company": "Acme Corp",
    "company_url": "https://acme.com",
    "linkedin": "https://linkedin.com/in/janesmith",
    "industry": "SaaS",
    "company_size": "SMB",
    "employee_count": 120,
    "score": 85,
    "discovered": "2025-01-15",
    "enrichment": {
      "funding": "Series B, $15M",
      "hiring": true,
      "tech_stack": ["React", "Python", "AWS"],
      "recent_news": "Launched enterprise plan Q4 2024"
    },
    "notes": "Strong ICP match, actively growing"
  }
]

Markdown Table Format

| # | Name | Title | Company | Score | Key Signal |
|---|------|-------|---------|-------|------------|
| 1 | Jane Smith | VP Engineering | Acme Corp | 85 | Series B funded, hiring |
| 2 | John Doe | CTO | Beta Inc | 72 | Product launch Q1 2025 |

Compliance & Ethics

DO

  • Use only publicly available information
  • Respect robots.txt and rate limits
  • Include data provenance (where each piece of info came from)
  • Allow users to export and delete their lead data
  • Clearly mark confidence levels on enriched data

DO NOT

  • Scrape behind login walls or paywalls
  • Fabricate any lead data (even "likely" email addresses without evidence)
  • Store sensitive personal data (SSN, financial info, health data)
  • Send unsolicited communications on behalf of the user
  • Bypass anti-scraping measures (CAPTCHAs, rate limits)
  • Collect data on individuals who have opted out of data collection

Data Retention

  • Keep lead data in local files only — never exfiltrate
  • Mark stale leads (>90 days without activity) for review
  • Provide clear data export in all supported formats

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